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Instructor
Dr. Ann E.K.  Um, Instructor - Introduction to Healthcare Analytics

Dr. Ann E.K. Um

Dr. Um is a statistician, with an MA from Stanford, a masters and a doctoral degree from Columbia. She was Data Science Manager at Harvard Medical School, Brigham and Women’s Hospital. She is the President and CEO at AMSTAT Consulting. She has over 20 years of experience working in and with the healthcare analytics industry.

Instructor: Dr. Ann E.K. Um

Healthcare Analytics: Concepts, Definitions, Technologies, and Implementations

  • This course will help you understand healthcare analytics and its technology components.
  • No technical knowledge is needed
  • Instructor has a doctorate degree from Columbia and masters from Stanford with 20+ years of experience working in and with the healthcare analytics industry.

Duration: 32m

Course Description

This course is targeted at individuals who want to understand what healthcare analytics is, how data analytics can be used in healthcare, and how it is implemented. The course has 5 parts: Analytics Primer Areas Currently Utilizing Predictive Analytics Healthcare Transformation Model Starting a Predictive Analytics Program The Analytics Program Lifecycle Challenges to Implementing Analytics

What am I going to get from this course?

Understand what healthcare analytics is, identify the 5-stage Analytics Program Lifecycle, and identify four types of analytics (i.e., descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics) and several areas currently benefiting from predictive analytics. 

 


 

Prerequisites and Target Audience

What will students need to know or do before starting this course?

No technical knowledge is needed. This course is an introduction and an overview of healthcare analytics.
 

 

Who should take this course? Who should not?

Anyone who wants an easy introduction to healthcare analytics and gain understanding of the technology components.

Curriculum

Module 1: Analytics Primer

04:56
Lecture 1 What Are Four Types of Analytics?
03:30

Introduces four types of analytics (i.e., descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics).

Lecture 2 How Can Analytics Be Used in Healthcare?
01:26

Gives examples of how analytics can be used in healthcare.

Quiz 1 Module 1 Quiz

Module 2: Areas Currently Utilizing Predictive Analytics

10:17
Lecture 3 Several Areas Currently Benefiting from Predictive Analytics
01:01

Introduces several areas currently benefiting from predictive analytics.

Lecture 4 Reduced Readmissions
03:31

Describes how predictive analytics can help reduce readmissions.

Lecture 5 Disease Outbreak Predictions
00:30

Describes how predictive analytics can be used to foresee an increase in influenza cases.

Lecture 6 Improving Patient Flow
02:20

Describes how predictive analytics can be used to manage hospital admissions and discharges.

Lecture 7 Emergency Room Uses
02:55

Describes how predictive analytics can be used to predict whether an emergency room patient is likely to: go into cardiac arrest; suffer a stroke; or potentially suffer from sepsis shock.

Quiz 2 Module 2 Quiz

Module 3: Healthcare Transformation Model

05:52
Lecture 8 What is the Healthcare Transformation Change Model?
01:58

Introduces the healthcare transformation change model.

Lecture 9 What are Three Continuums to the Healthcare Transformation Change Model?
03:54

Introduces three continuums to the healthcare transformation change model (i.e., organization/people, data/technology, process/workflow).

Quiz 3 Module 3 Quiz

Module 4: Starting a Predictive Analytics Program

04:40
Lecture 10 A Successful Predictive Analytics Implementation
04:40

Describes a successful predictive analytics implementation.

Quiz 4 Module 4 Quiz

Module 5: The Analytics Program Lifecycle

02:52
Lecture 11 What is the 5-stage Analytics Program Lifecycle (APL)?
00:55

Introduces the 5-stage Analytics Program Lifecycle (APL).

Lecture 12 Pre-Analysis
00:25

Describes the initial research stage.

Lecture 13 Data Gathering
00:31

Describes data gathering.

Lecture 14 Execution
00:15

Describes execution.

Lecture 15 Post-Analysis
00:30

Describes post-analysis.

Lecture 16 Adjustment
00:16

Describes the adjustment stage.

Quiz 5 Module 5 Quiz

Module 6: Challenges to Implementing Analytics

02:59
Lecture 17 Leadership Challenges
01:12

Introduces leadership challenges.

Lecture 18 Data Management Challenges
00:31

Introduces data management challenges.

Lecture 19 Talent Challenges
01:16

Introduces talent challenges.

Quiz 6 Module 6 Quiz
350